Systems currently exist that support collection of television data that identifies broadcast television programs that have been watched through a particular client device, such as a cable television set-top box. Furthermore, systems currently exist that automatically generate broadcast television recommendations based on the television data that is gathered. For example, a television recommendation system may recommend broadcast television programs to a particular viewer based on a comparison of television data associated with the particular viewer and television data associated with other viewers, resulting in a “other viewers who watched television program X also watched television program Y” type of recommendation.
Additionally, video-on-demand (VOD) systems currently exist that record data that identifies VOD purchases associated with a particular viewer. Similar to the broadcast television recommendations, VOD recommendations may be generated based on the gathered VOD data. However, VOD differs from broadcast television programs in that new VOD titles may become available periodically (e.g., monthly or weekly), and older VOD titles may no longer be available, and viewers typically watch a significantly fewer number of VOD titles than they do broadcast television programs. These two factors combined result in a much thinner VOD data set when compared to the television data set, and therefore results in less meaningful VOD recommendations.
Accordingly, a need exists for techniques for generating meaningful VOD recommendations, even when historical VOD data for a particular viewer is thin or non-existent.